Texas has one of the highest vaccination rates for childhood diseases overall, 97.4%, according to CDC. But the number of children not vaccinated because of their parents’ “personal beliefs”—as opposed to medical reasons—has risen since 2003, when such exemptions were introduced, to more than 44,000 so far in 2017 according to CDC. The 4:3:1:3:3:1:4 series is an overall measure that encompasses many vaccines that are recommended for children. Various demographic factors (sex, gender, race, availability of commercial health insurance) influence the decision to get vaccinated, were looked at.

The county-level data on the socioeconomic factors were obtained from US Census Bureau (American Factfinder). The health insurance data was obtained from Small Area Health Insurance Estimates (SAHIE). The vaccination rates were obtained from Texas Immunization registry through DSHS. The data was cleaned and geocoded to be analyzed in ArcGIS to produce maps as shown in Figure 1. Pearson’s correlation coefficient was used to analyze the relationship between vaccination rates and independent variable. The non-vaccination rates are higher around the major cities of Dallas, Austin-San Antonio, Houston and some northwest Texas counties. Population density has a positive correlation with the non-vaccination rate. Other demographic factors have a positive correlation in certain counties as opposed to others.
Source: American FactFinder, Texas Immunisation RegistryThe limitation on the immunization data is it being an optional registry so it would not be accurate to run statistics off this information to estimate an immunization rate. In future, it is productive to expand this concept to use regression analysis to try to find the odds of the relationship expressed in the maps and to find if there is a significant association.

There are several factors that contribute to obesity, one of the prominent ones is walkability. With the second world countries, having more space in general, they lack the environment that promotes walkability. These countries are well equipped with parks but they lack the facility for easy commute. One of the articles below shows the influence of walkability on the obesogenic environment.

The above maps compare the number of intersections in a European city vs Los Angeles and also Irvine, CA. Irvine is a city that is lush with many parks but unfortunately, it does not help people who could commute to places through walking.

This is an eye opener for city planners as such as a challenge for them to try to reduce obesogenic environment which would fit other lifestyle choices.

GIS a relatively new and is finding its way to improve health. Developing countries deal with more technological challenges to make use of GIS. But nonetheless, a very effective project with implication and impact on the local community of Ahmedabad, India is shown below. The project was impactful – was able to communicate the uneducated & also the educated mass on the issue. It went on to be heard by local policymakers too.

The above map developed by UMC in help with AMC staff shows the spatial distribution of slums and community health centers across the city.

UMC staff visited AMC’s existing UHCs and CHCs to understand their functioning and understand the requirements for upgrading facilities. UMC developed a methodology which involved meetings and interviews with health staff including medical officers, pharmacists, lab technicians, multi-purpose workers (MPW) etc. Separate SWOT analysis was conducted with medical health workers and with pharmacists, lab technicians and MPWs. UMC with assistance of AMC staff, identified slum pockets and communities on the base map of Ahmedabad and also marked existing health facilities provided by the AMC. These maps were later transferred to a GIS environment to analyze the accessibility of health facilities by slum dwellers. This assisted in locating newer health facilities as proposed under the NUHM. UMC also has prepared model layouts for the new proposed health centres for the AMC. A detailed phase-wise budget and a proposal was prepared for the AMC for submission to Government of India under the NUHM.

GIS is a powerful tool with various applications. One very useful of such is the ability to produce proximity analysis. It essentially gives an idea of how close one variable is to another, cartographically. For example, look into the below map.

Proximity Analysis of Injury with playgrounds, Pittsburg, PA

I created the above map using the tutorials in ESRI website for ArcGIS & the content from GIS Tutorial for Health. The above map shows the location of injury to residents and looks for how it is related to the playgrounds in the city. A simple analysis using ArcGIS software yielded the following results:

This helps us have an idea that most of the injuries happen away (1200 feet away, if not atleast 600 feet away) from the playground. Another interpretation of the results would be: 16% more injuries occur more than 600 feet away from the playground as compared to within 600 feet. we can conclude by simple spatial reading that playgrounds have a probable protective effect against injuries. Let promote playground on the week of the French Opens!

Texas has one of the highest vaccination rates for childhood diseases overall, 97.4%, according to CDC. But the number of children not vaccinated because of their parents’ “personal beliefs”—as opposed to medical reasons—has risen since 2003, when such exemptions were introduced, to more than 44,000 so far in 2017 according to CDC. The 4:3:1:3:3:1:4 series is an overall measure that encompasses many vaccines that are recommended for children. Various demographic factors (sex, gender, race, availability of commercial health insurance) influence the decision to get vaccinated, were looked at.

The county-level data on the socioeconomic factors were obtained from US Census Bureau (American Factfinder). The health insurance data was obtained from Small Area Health Insurance Estimates (SAHIE). The vaccination rates were obtained from Texas Immunization registry through DSHS. The data was cleaned and geocoded to be analyzed in ArcGIS to produce maps as shown in Figure 1. Pearson’s correlation coefficient was used to analyze the relationship between vaccination rates and independent variable.

The non-vaccination rates are higher around the major cities of Dallas, Austin-San Antonio, Houston and some northwest Texas counties. Population density has a positive correlation with the non-vaccination rate. Other demographic factors have a positive correlation in certain counties as opposed to others.

The limitation on the immunization data is it being an optional registry so it would not be accurate to run statistics off this information to estimate an immunization rate. In future, it is productive to expand this concept to use regression analysis to try to find the odds of the relationship expressed in the maps and to find if there is a significant association.

This CDC map displays stroke death rates from 2011-2013 in adults ages sixty-five and older. The data is taken from the National Vital Statistics System and the National Center for Health Statistics. This particular map shows all ethnic groups, and if you visit the site you can see other maps that focus on one ethnicity. Go to cdc.gov to see all the maps and to view more information !

all information for this post from cdc.gov. Click here to see the site. Contact gis@vertices.com.